{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "(MR-)CNP.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxVTz4-ObQ8t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Copyright 2019 Google LLC.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\")\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from collections import namedtuple\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import time\n",
        "import collections\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "import tensorflow_probability as tfp\n",
        "from tensorflow_probability.python.layers import util as tfp_layers_util\n",
        "\n",
        "tf.enable_v2_tensorshape()\n",
        "\n",
        "NeuralProcessParams = collections.namedtuple('NeuralProcessParams',\n",
        "                                             ['dim_r', 'dim_z','dim_w',\n",
        "                                              'n_hidden_units_r',\n",
        "                                              'n_hidden_units_g'])\n",
        "GaussianParams = collections.namedtuple('GaussianParams', ['mu', 'sigma'])\n",
        "cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "595zp5kimJQH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#choose the model here\n",
        "exp_type = 'CNP' # choose from 'CNP', 'MR-CNP-A', 'MR-CNP-W'\n",
        "\n",
        "noise_scale = 0.1 #@param {type:\"number\"}\n",
        "n_obs = 20 #@param {type:\"number\"}\n",
        "n_context = 10 #@param {type:\"number\"}\n",
        "K_amp = 20 #@param {type:\"number\"}\n",
        "x_width = 5 #@param {type:\"number\"}\n",
        "learning_rate= 0.001 #@param {type:\"number\"}\n",
        "n_iter = 30000 #@param {type:\"number\"}\n",
        "amps = np.linspace(0.1,4,K_amp)\n",
        "params = \\\n",
        "NeuralProcessParams(dim_r=10, dim_z=5, dim_w = 5,\n",
        "                    n_hidden_units_r=[100,100],\n",
        "                    n_hidden_units_g=[100,100]) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AL-Qce4wlNE6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# auxilliary functions\n",
        "\n",
        "def split_context_target(xs, ys, n_context,\n",
        "                         context_xs, context_ys, target_xs, target_ys):\n",
        "  \"\"\"\n",
        "    split samples randomly into task training and task validation sets.\n",
        "  \"\"\"\n",
        "  indices = set(range(ys.shape[0]))\n",
        "  context_set_indices = set(random.sample(indices, n_context))\n",
        "  target_set_indices = indices - context_set_indices\n",
        "\n",
        "  return {\n",
        "      context_xs: xs[list(context_set_indices), :],\n",
        "      context_ys: ys[list(context_set_indices), :],\n",
        "      target_xs: xs[list(target_set_indices), :],\n",
        "      target_ys: ys[list(target_set_indices), :]\n",
        "  }\n",
        "\n",
        "def sampling(output):\n",
        "  \"\"\"\n",
        "    sample from Gaussian Distribution\n",
        "  \"\"\"\n",
        "  mu, logstd = tf.split(output, num_or_size_splits=2, axis=-1)\n",
        "  sigma = tf.nn.softplus(logstd)\n",
        "  ws = mu + tf.random_normal(tf.shape(mu)) * sigma\n",
        "  return ws, mu, sigma\n",
        "\n",
        "def merge(A, B):\n",
        "  '''A is [n, k1], B is [m, k2], return [n, m, (k1+k2)]\n",
        "  '''\n",
        "  A_repeat = tf.expand_dims(A, axis=1)\n",
        "  A_repeat = tf.tile(A_repeat, [1, tf.shape(B)[0], 1])\n",
        "\n",
        "  B_repeat = tf.expand_dims(B, axis=0)\n",
        "  B_repeat = tf.tile(B_repeat, [tf.shape(A)[0], 1, 1])\n",
        "\n",
        "  return tf.concat([A_repeat, B_repeat], axis=2)\n",
        "\n",
        "def kl_qp_gaussian(mu_q, sigma_q, mu_p, sigma_p):\n",
        "  \"\"\"KL(N(mu_q), Diag(sigma_q^2) || N(mu_p), Diag(sigma_p^2))\"\"\"\n",
        "  sigma2_q = tf.square(sigma_q) + 1e-16\n",
        "  sigma2_p = tf.square(sigma_p) + 1e-16\n",
        "  temp = tf.log(sigma2_p) - tf.log(sigma2_q) - 1.0 + \\\n",
        "          sigma2_q / sigma2_p + tf.square(mu_q - mu_p) / sigma2_p  #N*D\n",
        "  kl = 0.5 * tf.reduce_sum(temp, axis = 1)\n",
        "  return tf.reduce_mean(kl)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuUx4BmvgSil",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# (MR-)CNP model\n",
        "\n",
        "if exp_type == 'MR-CNP-W':\n",
        "  kernel_posterior_fn=tfp_layers_util.default_mean_field_normal_fn(untransformed_scale_initializer=tf.compat.v1.initializers.random_normal(\n",
        "    mean=-3., stddev=0.1))\n",
        "  encoder_w = tf.keras.Sequential([\n",
        "          tfp.layers.DenseReparameterization(100, activation=tf.nn.relu, kernel_posterior_fn = kernel_posterior_fn),\n",
        "          tfp.layers.DenseReparameterization(params.dim_w),\n",
        "        ])\n",
        "\n",
        "if exp_type == 'MR-CNP-A':\n",
        "  def encoder_w(xas, params):\n",
        "    hidden_layer = xas\n",
        "    hidden_layer = tf.layers.dense(hidden_layer, 100,\n",
        "                                  activation=tf.nn.relu,\n",
        "                                  name='encoder_w_{}'.format(0),\n",
        "                                  reuse=tf.AUTO_REUSE,\n",
        "                                  kernel_initializer='normal')\n",
        "    # Last layer is simple linear\n",
        "    mu_w = tf.layers.dense(hidden_layer, params.dim_w, name='w_params_mu',\n",
        "                        reuse=tf.AUTO_REUSE, kernel_initializer='normal')\n",
        "\n",
        "    sigma_w = tf.layers.dense(hidden_layer, params.dim_w, name='w_params_sigma',\n",
        "                            reuse=tf.AUTO_REUSE,\n",
        "                            kernel_initializer='normal')\n",
        "    sigma_w = tf.nn.softplus(sigma_w)\n",
        "    return mu_w, sigma_w\n",
        "\n",
        "def encoder_r(xyas, params):\n",
        "  \"\"\"\n",
        "    encode task training data.\n",
        "  \"\"\"\n",
        "  hidden_layer = xyas\n",
        "  for i, n_hidden_units in enumerate(params.n_hidden_units_r):\n",
        "    hidden_layer = tf.layers.dense(hidden_layer, n_hidden_units,\n",
        "                                   activation=tf.nn.relu,\n",
        "                                   name='encoder_layer_{}'.format(i),\n",
        "                                   reuse=tf.AUTO_REUSE,\n",
        "                                   kernel_initializer='normal')\n",
        "\n",
        "  # Last layer is linear\n",
        "  i = len(params.n_hidden_units_r)\n",
        "  r = tf.layers.dense(hidden_layer, params.dim_r,\n",
        "                      name='encoder_layer_{}'.format(i),\n",
        "                      reuse=tf.AUTO_REUSE,\n",
        "                      kernel_initializer='normal')\n",
        "  return r\n",
        "\n",
        "\n",
        "def xy_to_z_params(xs, ys, amplitude, params, encoder_w=None):\n",
        "  '''\n",
        "    Aggregator of task training data.\n",
        "    i) rs = T1(xas, ys); ii) r = mean(rs) iii)z = T2(r)\n",
        "  '''\n",
        "  # i)\n",
        "  amplitude = tf.tile(amplitude, [tf.shape(xs)[0], 1])\n",
        "  if exp_type == 'CNP':\n",
        "    xyas = tf.concat([xs, ys, amplitude], axis=1)\n",
        "    rs = encoder_r(xyas, params)\n",
        "\n",
        "  if exp_type == 'MR-CNP-W':\n",
        "    xas = tf.concat([xs, amplitude], axis=1)\n",
        "    ws = encoder_w(xas)\n",
        "    wys = tf.concat([ws, ys], axis=1)\n",
        "    rs = encoder_r(wys, params)\n",
        "\n",
        "  if exp_type == 'MR-CNP-A':\n",
        "    xas = tf.concat([xs, amplitude], axis=1)\n",
        "    mu_w,  sigma_w= encoder_w(xas, params)\n",
        "    ws = mu_w + tf.random_normal(tf.shape(mu_w)) * sigma_w\n",
        "    wys = tf.concat([ws, ys], axis=1)\n",
        "    rs = encoder_r(wys, params)\n",
        "  # ii)\n",
        "  r = tf.reshape(tf.reduce_mean(rs, axis=0), [1, -1])\n",
        "  # iii)\n",
        "  z_sample = tf.layers.dense(r, params.dim_z, name='z_params_mu',\n",
        "                      reuse=tf.AUTO_REUSE, kernel_initializer='normal')\n",
        "  if exp_type == 'MR-CNP-A':\n",
        "    return z_sample, mu_w, sigma_w\n",
        "  else:\n",
        "    return z_sample\n",
        "\n",
        "\n",
        "\n",
        "def decoder_g(zws, params, activation = None):\n",
        "  '''\n",
        "    y_hat =  G(context, task testing input)\n",
        "  '''\n",
        "  hidden_layer = zws\n",
        "  for i, n_hidden_units in enumerate(params.n_hidden_units_g):\n",
        "    hidden_layer = tf.layers.dense(hidden_layer, n_hidden_units,\n",
        "                                  activation=tf.nn.relu,\n",
        "                                  name='decoder_layer_{}'.format(i),\n",
        "                                  reuse=tf.AUTO_REUSE,\n",
        "                                  kernel_initializer='normal')\n",
        "\n",
        "  # Last layer is linear\n",
        "  i = len(params.n_hidden_units_g)\n",
        "  y_hat = tf.layers.dense(hidden_layer, 1,\n",
        "                                name='decoder_layer_{}'.format(i),\n",
        "                                reuse=tf.AUTO_REUSE,\n",
        "                                kernel_initializer='normal')\n",
        "  return y_hat\n",
        "\n",
        "def neg_loglikelihood(y_star, mu_star, noise_scale=noise_scale):\n",
        "  p_normal = tf.distributions.Normal(loc=mu_star, scale=noise_scale)\n",
        "  loglike = p_normal.log_prob(y_star) #n_target * n_z\n",
        "  loglike = tf.reduce_sum(loglike, axis=0)\n",
        "  loglike = tf.reduce_mean(loglike)\n",
        "  return -loglike\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cG4tFtXZmMaT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "# Placeholders for training inputs\n",
        "context_xs = tf.placeholder(tf.float32, (None, 1))\n",
        "context_ys = tf.placeholder(tf.float32, (None, 1))\n",
        "target_xs = tf.placeholder(tf.float32, (None, 1))\n",
        "target_ys = tf.placeholder(tf.float32, (None, 1))\n",
        "amplitude = tf.placeholder(tf.float32, (1, K_amp))\n",
        "if exp_type == 'MR-CNP-W':\n",
        "  Beta = tf.placeholder_with_default(0.15, ())\n",
        "if exp_type == 'MR-CNP-A':\n",
        "  Beta = tf.placeholder_with_default(5.0, ())\n",
        "\n",
        "if exp_type == 'MR-CNP-A':\n",
        "  z_samples, mu_w, sigma_w =  xy_to_z_params(context_xs, context_ys, amplitude, params, encoder_w)\n",
        "else:\n",
        "  z_samples =  xy_to_z_params(context_xs, context_ys, amplitude, params, encoder_w)\n",
        "\n",
        "target_xas = tf.concat([target_xs, tf.tile(amplitude, [tf.shape(target_xs)[0], 1])], axis=1)\n",
        "\n",
        "if exp_type == 'CNP':\n",
        "  input_target = merge(z_samples, target_xas)\n",
        "\n",
        "if exp_type == 'MR-CNP-W':\n",
        "  with tf.variable_scope('encoder_w'):\n",
        "    target_ws = encoder_w(target_xas)\n",
        "  input_target = merge(z_samples, target_ws) #n_z * n_target * (d_z + d_w)\n",
        "\n",
        "if exp_type == 'MR-CNP-A':\n",
        "  target_mu_w, target_sigma_w = encoder_w(target_xas, params)\n",
        "  target_ws = target_mu_w + tf.random_normal(tf.shape(target_mu_w)) * target_sigma_w\n",
        "  input_target = merge(z_samples, target_ws) #n_z * n_target * (d_z + d_w)\n",
        "\n",
        "# sample y_hat ~  y|(w,z)\n",
        "y_star_mu_test = decoder_g(input_target, params)\n",
        "y_star_mu_test = tf.squeeze(y_star_mu_test, axis=2)\n",
        "y_star_mu_test = tf.transpose(y_star_mu_test)  #n_target * n_z\n",
        "\n",
        "\n",
        "# loss & optimizer\n",
        "neg_loglike = neg_loglikelihood(target_ys, y_star_mu_test)\n",
        "if exp_type == 'CNP':\n",
        "  loss = neg_loglike\n",
        "if exp_type == 'MR-CNP-W':\n",
        "  kl_loss = Beta * sum(encoder_w.losses)\n",
        "  loss = neg_loglike + kl_loss\n",
        "\n",
        "if exp_type == 'MR-CNP-A':\n",
        "  kl_ib = kl_qp_gaussian(mu_w, sigma_w,\n",
        "                        tf.zeros(tf.shape(mu_w)), tf.ones(tf.shape(mu_w)))\n",
        "  loss = neg_loglike + Beta * kl_ib\n",
        "\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
        "train_op = optimizer.minimize(loss)\n",
        "\n",
        "\n",
        "def train_model(sess, n_iter = n_iter, verbose = False):\n",
        "  cost = []; cost_r = []\n",
        "  for i in range(n_iter):\n",
        "      #generate data\n",
        "      xs = np.random.uniform(-x_width, x_width, n_obs)\n",
        "      amp_ind = random.randint(0,K_amp-5)\n",
        "      amp = np.zeros([1, K_amp]); amp[0,amp_ind] = 1\n",
        "      ys = amps[amp_ind] * np.sin(xs) + np.random.normal(scale = noise_scale, size = xs.shape)\n",
        "      feed_dict = split_context_target(xs.reshape(-1, 1), ys.reshape(-1, 1),\n",
        "                                      n_context, context_xs, context_ys, target_xs, target_ys)\n",
        "      feed_dict.update({amplitude:amp})\n",
        "\n",
        "      #training\n",
        "      A = sess.run((train_op, loss), feed_dict=feed_dict)\n",
        "      cost.append(A[1])\n",
        "\n",
        "      if verbose and i%5000 == 0:\n",
        "        cost_r.append(np.mean(cost))\n",
        "        print('iter=', i, \"Loss: {:.3f}\".format(np.mean(cost)))\n",
        "        cost = []\n",
        "  return sess\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_cfN_s4mMKB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_out(sess, amp_ind, n_context=n_context, amps = amps, width = x_width, ax = None, seed=None, K_test_sample=100, legend = False):\n",
        "    if seed is not None:\n",
        "      np.random.seed(seed**2)\n",
        "    A = np.random.uniform(low = amps[0], high = amps[-1])\n",
        "    a_onehot = np.zeros([1, K_amp]); a_onehot[0,amp_ind] = 1\n",
        "    #task training\n",
        "    xc_test = np.reshape(np.random.uniform(-width, width, n_context),[-1, 1])\n",
        "    yc_test = A*np.sin(xc_test) + np.random.normal(scale = noise_scale, size = xc_test.shape)\n",
        "    #task validation\n",
        "    xs_test = np.reshape(np.linspace(-5, 5, 100),[-1, 1])\n",
        "    ys_test = A*np.sin(xs_test)\n",
        "\n",
        "    mean_curve = 0\n",
        "    for i in range(K_test_sample):\n",
        "      sample_curves = sess.run(y_star_mu_test, feed_dict = {context_xs:xc_test , context_ys:yc_test ,\n",
        "                              target_xs:xs_test, target_ys:ys_test, amplitude:a_onehot})\n",
        "      mean_curve += sample_curves[:,0] / (K_test_sample*1.0)\n",
        "    error = np.mean(np.square(mean_curve - ys_test[:,0]))\n",
        "\n",
        "    if ax is not None:\n",
        "      plt.sca(ax)\n",
        "      plt.plot(xs_test,ys_test,'-', color='darkorange', linewidth=2.0, label = 'True Function', zorder=2)\n",
        "      plt.plot(xs_test,mean_curve,'b-', linewidth=2.0, label = 'Prediction', zorder=2)\n",
        "      plt.ylim([-6,6])\n",
        "      plt.axvline(x=-width, linestyle='--')\n",
        "      plt.axvline(x=width,linestyle='--')\n",
        "      plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "      plt.plot(xc_test,yc_test, '^',  label = 'Context Points', zorder=3)\n",
        "      if legend:\n",
        "        plt.legend( loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "    return error, A"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLZgC-cCr7Ge",
        "colab_type": "text"
      },
      "source": [
        "#Training & Testing for CNP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77kSeqXgr6fE",
        "colab_type": "code",
        "outputId": "a0fe9fa6-0764-49df-90a0-9cb724727783",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "sess = train_model(sess, n_iter=n_iter, verbose=False)\n",
        "\n",
        "\n",
        "print(\"Model is\", exp_type)\n",
        "n_test_task = 100\n",
        "n_context_test = 5\n",
        "errors = []\n",
        "for i in range(n_test_task):\n",
        "  error, A = plot_out(sess, amp_ind = random.randint(5,K_amp-5), n_context=n_context_test, seed = i)\n",
        "  errors.append(error)\n",
        "print(\"n_context = \", n_context_test, \"error =\", np.mean(errors))\n",
        "\n",
        "n_context_test = 10\n",
        "errors = []\n",
        "for i in range(n_test_task):\n",
        "  error, A = plot_out(sess, amp_ind = random.randint(5,K_amp-5), n_context=n_context_test, seed = i)\n",
        "  errors.append(error)\n",
        "print(\"n_context = \", n_context_test, \"error =\", np.mean(errors))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model is CNP\n",
            "n_context =  5 error = 0.9669236715483034\n",
            "n_context =  10 error = 0.8186722369121264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEq5_IIDt6D-",
        "colab_type": "code",
        "outputId": "3b522a70-6624-4f56-d10c-af1ab10c8543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "fig, ax = plt.subplots(ncols=4, figsize=(16,4))\n",
        "a_onehot = 15\n",
        "error, A = plot_out(sess, a_onehot, seed = 1,  ax = ax[0]);\n",
        "error, A  = plot_out(sess, a_onehot, seed = 3,  ax = ax[1]);\n",
        "error, A = plot_out(sess, a_onehot, seed = 5,  ax = ax[2]);\n",
        "error, A = plot_out(sess, a_onehot,  seed = 15,  ax = ax[3], legend='True');\n",
        "for ax_i in ax:\n",
        "    ax_i.tick_params(axis='both', which='major',length=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5vVkuRu/WeSl+/FGm/ZXGmjWwZYseudGvn+/2e9dd+vu338q0P+F/ypw4UErt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pD5TO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\nEEIU6P8Bup8nQX/Vkh8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Prpp-yz_csuz",
        "colab_type": "text"
      },
      "source": [
        "#Training & Testing for MR-CNP(A)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufOYpg9Pvddd",
        "colab_type": "code",
        "outputId": "f52b4291-f472-45c1-cbbd-5847152da92e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "sess = train_model(sess, n_iter=n_iter, verbose=False)\n",
        "\n",
        "\n",
        "print(\"Model is\", exp_type)\n",
        "n_test_task = 100\n",
        "n_context_test = 5\n",
        "errors = []\n",
        "for i in range(n_test_task):\n",
        "  error, A = plot_out(sess, amp_ind = random.randint(5,K_amp-5), n_context=n_context_test, seed = i)\n",
        "  errors.append(error)\n",
        "print(\"n_context = \", n_context_test, \"error =\", np.mean(errors))\n",
        "\n",
        "n_context_test = 10\n",
        "errors = []\n",
        "for i in range(n_test_task):\n",
        "  error, A = plot_out(sess, amp_ind = random.randint(5,K_amp-5), n_context=n_context_test, seed = i)\n",
        "  errors.append(error)\n",
        "print(\"n_context = \", n_context_test, \"error =\", np.mean(errors))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model is MR-CNP-A\n",
            "n_context =  5 error = 0.04330778675580867\n",
            "n_context =  10 error = 0.03515166627173694\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJHasbZic4O_",
        "colab_type": "code",
        "outputId": "d4df6086-ed2f-4cb9-d030-de9e10af99aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "fig, ax = plt.subplots(ncols=4, figsize=(16,4))\n",
        "a_onehot = 15\n",
        "error, A = plot_out(sess, a_onehot, seed = 1,  ax = ax[0]);\n",
        "error, A  = plot_out(sess, a_onehot, seed = 3,  ax = ax[1]);\n",
        "error, A = plot_out(sess, a_onehot, seed = 5,  ax = ax[2]);\n",
        "error, A = plot_out(sess, a_onehot,  seed = 15,  ax = ax[3], legend='True');\n",
        "for ax_i in ax:\n",
        "    ax_i.tick_params(axis='both', which='major',length=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fajuOLVoErVrpMDAXKE592my2mdWrV28aGBiYabFYZLqagTmdTiUtLS0kJSVl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\nkTEtufVWWL3auGsNXa5GY2JiElq1apW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WERFxWx936dIlsUuXLsfufGyPHj2u9ejR4z89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\nly9fznb8mzdvPh4eHn50+fLlkffbv+BWLi4ut8ar3y1eZ2d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j8+6NN1KobFWDozGhIt5Q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\nhBBCCCGyJAMHQgghhBBCCCGEyNL/A8JCtv0WQgrPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEhBbmxJc6qK",
        "colab_type": "text"
      },
      "source": [
        "#Training & Testing for MR-CNP(W)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkZWD0huc94j",
        "colab_type": "code",
        "outputId": "c3c6715b-f362-4e77-b206-7b55876bfadf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "n_iter = 120000\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "sess = train_model(sess, n_iter=n_iter, verbose=False)\n",
        "\n",
        "\n",
        "print(\"Model is\", exp_type)\n",
        "n_test_task = 100\n",
        "n_context_test = 5\n",
        "errors = []\n",
        "for i in range(n_test_task):\n",
        "  error, A = plot_out(sess, amp_ind = random.randint(5,K_amp-5), n_context=n_context_test, seed = i)\n",
        "  errors.append(error)\n",
        "print(\"n_context = \", n_context_test, \"error =\", np.mean(errors))\n",
        "\n",
        "n_context_test = 10\n",
        "errors = []\n",
        "for i in range(n_test_task):\n",
        "  error, A = plot_out(sess, amp_ind = random.randint(5,K_amp-5), n_context=n_context_test, seed = i)\n",
        "  errors.append(error)\n",
        "print(\"n_context = \", n_context_test, \"error =\", np.mean(errors))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model is MR-CNP-W\n",
            "n_context =  5 error = 0.06632440420899471\n",
            "n_context =  10 error = 0.05497082944476869\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rm2IK3NEc9rO",
        "colab_type": "code",
        "outputId": "e9c3357c-3ca5-447d-be0f-c6b51798ae3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "fig, ax = plt.subplots(ncols=4, figsize=(16,4))\n",
        "a_onehot = 15\n",
        "error, A = plot_out(sess, a_onehot, seed = 1,  ax = ax[0]);\n",
        "error, A  = plot_out(sess, a_onehot, seed = 3,  ax = ax[1]);\n",
        "error, A = plot_out(sess, a_onehot, seed = 5,  ax = ax[2]);\n",
        "error, A = plot_out(sess, a_onehot,  seed = 15,  ax = ax[3], legend='True');\n",
        "for ax_i in ax:\n",
        "    ax_i.tick_params(axis='both', which='major',length=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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unIOXRu5iUPXPqZSxCHLTb3hMjt3GroT7WRD1LN9v6MyWLa706qXNSHj/fejY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\nhBBCCCGEuCUZOBBCCCGEEEIIIcQt/R+C+ox4jOFMtwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zj_JLympfobO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
